Umeå University's logo

umu.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Advances in machine learning for agricultural robots
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Örebro University, Örebro, Sweden.ORCID-id: 0000-0003-4685-379X
Örebro University, Örebro, Sweden.ORCID-id: 0000-0003-3788-499X
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-4600-8652
2024 (engelsk)Inngår i: Advances in agri-food robotics / [ed] Eldert van Henten; Yael Edan, Cambridge: Burleigh Dodds Science Publishing , 2024, s. 103-134Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

This chapter presents a survey of the advances in using machine learning algorithms for agricultural robotics. The development of machine learning algorithms in the last decade has been astounding, and there has therefore been a rapid increase in the widespread deployment of machine learning algorithms in many domains, such as agricultural robotics. However, there are also major challenges to be overcome in ML for agri-robotics, due to the unavoidable complexity and variability of the operating environments, and the difficulties in accessing the required quantities of relevant training data. This chapter presents an overview of the usage of ML for agri-robotics and discusses the use of ML for data analysis and decision-making for perception and navigation. It outlines the main trends of the last decade in employed algorithms and available data. We then discuss the challenges the field is facing and ways to overcome these challenges.

sted, utgiver, år, opplag, sider
Cambridge: Burleigh Dodds Science Publishing , 2024. s. 103-134
Serie
Burleigh dodds series in agricultural science, ISSN 2059-6936, E-ISSN 2059-6944 ; 139
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:umu:diva-223680DOI: 10.19103/AS.2023.0124.04ISBN: 9781801462778 (tryckt)ISBN: 9781801462792 (digital)ISBN: 9781801462785 (digital)OAI: oai:DiVA.org:umu-223680DiVA, id: diva2:1853847
Tilgjengelig fra: 2024-04-23 Laget: 2024-04-23 Sist oppdatert: 2024-05-14bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Person

Kurtser, PolinaRingdahl, Ola

Søk i DiVA

Av forfatter/redaktør
Kurtser, PolinaLowry, StephanieRingdahl, Ola
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 86 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf